Accurate gray and white matter segmentation of CT (Computer Tomography) images is important in the estimation of cerebrospinal fluid, white and gray matter regions, which in turn is useful in the prediction of clinical outcomes. For example, when identifying stroke from CT scan images, it is very important that the cerebrospinal fluid (CSF) and the white matter (WM) intensity thresholds are accurately identified. This is because, the infract intensity range is close to the intensity ranges of the hypo-dense WM (i.e. lower values of intensity amongst WM intensities) and the hyper-dense CSF (i.e. higher values of intensity amongst CSF intensities).
Unfortunately, inherent instrumentation effects in CT images often affect the image quality of the CT images and increase their variability. In addition, intensity variations in CT scan images can occur due to the following reasons: different machine parameters, beam hardening artifacts and signal attenuations due to different skull thickness. Further, due to infarction, there is usually a loss of contrast between gray and white matter in CT images [Choi et al. 2008, Torbey et al 2000]. This impacts the segmentation accuracy. Moreover, the contrast between gray matter (GM) and white matter (WM) is very low, hence increasing the difficulty in segmenting the CT images. Thus, simply using hard coded threshold values to segment CT images can lead to substantial errors.
Several segmentation techniques have been previously proposed. Lee et al. 2008 proposed an approach of segmenting CT brain images using K-means and EM (Expectation-Maximization) clustering. However, the approach in Lee et al. 2008 is limited to the segmentation of the CSF (Cerebrospinal fluid), skull, parenchyma and calcification regions, and does not consider the segmentation of gray and white matter. Hu et al 2007 proposed an automatic segmentation method based on pattern recognition, fuzzy theory, anatomy, fractal dimensions etc. However, the method in Hu et al 2007 uses arbitrary parameters and has no quantitative measure of performance. Furthermore, in general, a shape or texture based analysis can perform well only when the image quality is good. Qian et al. 2007 proposed a method of extracting the brain from CT head scans based on domain knowledge. However, the method in Qian et al. 2007 is limited to skull removal and/or head extractions. Furthermore, Hacker and Artmann, 1976 proposed a ROI (Region of Interest) based CSF estimation whereby the intensity value of CSF is estimated to be approximately 20 HU.
Many of the segmentation algorithms are based on the adjustment of several parameters [Heydarian et al 2009] and this requires experience and expertise. Moreover, such segmentation algorithms have their limitations when used on an organ composed of different tissue types (e.g. the brain). In addition, although genetic algorithms have been proposed for segmentation purposes, there are often issues with the convergence criteria and the huge computation time of these algorithms [Maulik 2009]. Also, most commercially available algorithms are not developed with CT images in mind due to the poor image quality of CT images. For example, some algorithms (such as the SPM5 which works for MR images only) require registration to an initial template if they are to be used for CT images.